pandasnet
is a python package build on top of pythonnet
.
It provides additional data conversions for pandas
, numpy
and datetime
dotnet also provides scripts to proceed the installation by command line.
pip install pandasnet
To load the converter you need to import the package once in your python environment. If the dotnet clr isn't started yet through the pytonnet package the import will.
import pandasnet
We construct a simple C# function to test conversion
using System;
using System.Collections.Generic;
namespace LibForTests
{
public class PandasNet
{
public static Dictionary<string, Array> BasicDataFrame(Dictionary<string, Array> df)
=> df;
}
}
We build this function into a library named LibForTests.dll
.
We load this library into our python environment then use it.
import clr
import pandasnet # Load the converters
import pandas as pd
from datetime import datetime
# Load your dll
clr.AddReference('LibForTests.dll')
from LibForTests import PandasNet as pdnet
x = pd.DataFrame({
'A': [1, 2, 3],
'B': [1.23, 1.24, 1.22],
'C': ['foo', 'bar', 'other'],
'D': [datetime(2021, 1, 22), datetime(2021, 1, 23), datetime(2021, 1, 24)]
})
y = pdnet.BasicDataFrame(x)
print(y)
Below an exhausitve list of supported data convertions.
Python | .Net |
---|---|
datetime.datetime | DateTime |
datetime.date | DateTime |
datetime.timedelta | TimeSpan |
datetime.time | TimeSpan |
numpy.ndarray(dtype=bool_) | bool[] |
numpy.ndarray(dtype=int8) | sbyte[] |
numpy.ndarray(dtype=int16) | short[] |
numpy.ndarray(dtype=int32) | int[] |
numpy.ndarray(dtype=int64) | long[] |
numpy.ndarray(dtype=uint8) | byte[] |
numpy.ndarray(dtype=uint16) | ushort[] |
numpy.ndarray(dtype=uint32) | uint[] |
numpy.ndarray(dtype=uint64) | ulong[] |
numpy.ndarray(dtype=float32) | float[] |
numpy.ndarray(dtype=float64) | double[] |
numpy.ndarray(dtype=datetime64) | DateTime[] |
numpy.ndarray(dtype=timedelta64) | TimeSpan[] |
numpy.ndarray(dtype=str) | string[] |
pandas._libs.tslibs.timestamps.Timestamp | DateTime |
pandas._libs.tslibs.timedeltas.TimeDelta | TimeSpan |
pandas.core.series.Series | Array |
pandas.core.frame.DataFrame | Dictionary[string, Array] |
.Net | Python |
---|---|
DateTime | datetime.datetime |
TimeSpan | datetime.timedelta |
bool[] | numpy.ndarray(dtype=bool_) |
sbyte[] | numpy.ndarray(dtype=int8) |
short[] | numpy.ndarray(dtype=int16) |
int[] | numpy.ndarray(dtype=int32) |
long[] | numpy.ndarray(dtype=int64) |
byte[] | numpy.ndarray(dtype=uint8) |
ushort[] | numpy.ndarray(dtype=uint16) |
uint[] | numpy.ndarray(dtype=uint32) |
ulong[] | numpy.ndarray(dtype=uint64) |
float[] | numpy.ndarray(dtype=float32) |
double[] | numpy.ndarray(dtype=float64) |
DateTime[] | numpy.ndarray(dtype=datetime64) |
TimeSpan[] | numpy.ndarray(dtype=timedelta64) |
Dictionary[string, Array] | pandas.core.frame.DataFrame |
Issue tracker: https://github.com/fdieulle/pandasnet/issues
If you want to checkout the project and propose your own contribution, you will need to setup it following few steps:
python -m venv venv
venv/Scripts/activate
pip install -r requirements.txt
This project is open source under the MIT license.